However, there are basically two main problems that remained unsolved in the original work[1]}:
1) this monocular image based approach is not able to provide pose estimates in a consistent global scale. Due to the scale ambiguity, no physically meaningful global trajectory can be reconstructed, limiting its real use.
2) The photometric loss assumes that the scene is static and without camera occlusions. Although the authors proposed the use of an explainability mask to remove scene dynamics, the influence of these environmental factors is still not addressed completely, which violates the assumption. To address these concerns, an increasing number of works [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]} extended this unsupervised framework to achieve better performance.
